An antenna with electronic scanning can comprise a large number of active modules. Accordingly, in order to optimize the availability of a radar comprising an antenna with electronic scanning, the impact of the failure of one or more active modules on the main functions of the radar must be limited. It is thus desirable that the loss of several active modules does not compromise the receiving function of the radar in order to reach an optimum level of service. These constraints are justified notably when such a radar is used for applications requiring a high level of security and reliability of operation such as that required, for example, in the case of an airborne radar on a commercial aircraft, for example of the weather radar type.
In the case of a radar whose beam is formed by calculation, the full set of samples coming from the active modules is used in reception. When an active module is defective due to a malfunction or fault, the samples can no longer be employed for the formation of the beam without significantly degrading the reception performance of the radar. The tolerance to these failures of active modules can notably be improved by using interpolation methods for the spatial samples missing due to failures. The radar beam is then formed by calculation using the interpolations as if they were real samples.
For this purpose, there exist linear prediction methods which, by means of the valid samples coming from the active modules in nominal operation, allow the complete signal as it is received by the radar to be decomposed into a sum of sinusoidal signals with amplitudes and frequencies that said methods seek to estimate. Aside from the discrete Fourrier transform, which does not directly provide this decomposition, the other linear interpolation techniques require the estimation of covariance matrices. These adaptive techniques may be readily applied to antennas whose active modules are uniformly distributed over the surface of the antenna.
However, the estimation of covariance matrices is complex and imprecise on an antenna whose active modules are distributed according to a non-constant distribution law over the surface of the antenna. The linear prediction methods are therefore maladapted to this type of antenna due to their complexity and their cost.